Low‐complexity wireless sensor system for partial discharge localisation. (1st June 2019)

• Record Type: Journal Article
• Authors: Iorkyase, Ephraim T.; Tachtatzis, Christos; Lazaridis, Pavlos; Glover, Ian A.; Atkinson, Robert C.
• Is Part Of: IET wireless sensor systems. Volume 9:Number 3(2019)
• Page Start: 158
• Page End: 165
• Publication Date: 2019-06-01
• Subjects: learning (artificial intelligence) -- condition monitoring -- neural nets -- multilayer perceptrons -- wireless sensor networks -- partial discharge measurement -- substations -- power engineering computing -- nearest neighbour methods -- approximation theory -- regression analysis; generalised regression neural network -- PD localisation -- low‐complexity wireless sensor system -- partial discharge localisation -- wireless sensor network -- algorithmic software -- condition‐based monitoring -- electrical plant -- low‐complexity fingerprinting technique -- artificial neural network learning models -- wireless sensor system -- substation -- radio sensors -- multilayer perceptron -- weighted K‐nearest neighbour models; Wireless sensor networks -- Periodicals; 681.2
• DOI: 10.1049/iet-wss.2018.5075
• Languages: English
• ISSNs: 2043-6386

Abstract

Abstract : This study describes a key element of any modern wireless sensor system: data processing. The authors describe a system consisting of a wireless sensor network and an algorithmic software for condition‐based monitoring of electrical plant in a live substation. Specifically, the aim is to monitor for the presence of partial discharge (PD) using a matrix of inexpensive radio sensors with limited processing capability. A low‐complexity fingerprinting technique is proposed, given that the sensor nodes to be deployed will be highly constrained in terms of processing power, memory and battery life. Two variants of artificial neural network (ANN) learning models (multilayer perceptron and generalised regression neural network) that use regression as a form of function approximation are developed and their performance compared to K‐nearest neighbour and weighted K‐nearest neighbour models. The results indicate that the ANN models yield superior performance in terms of robustness against noise and may be particularly suited for PD localisation.